Implications and Preventions of Cyberbullying and Social Exclusion in Social Media: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: The growth of social networking has created a paradigm in which many forms of personal communication are being replaced by internet communication technologies, such as social media. This has led to social issues, such as cyberbullying. In response, researchers are investigating cyberbullying to determine its implications in various life sectors. OBJECTIVE: This manuscript reviews the methods, results, and limitations of the current cyberbullying research and discusses the physical and mental repercussions of cyberbullying and social exclusion as well as methods of predicting and counteracting these events. On the basis of the findings, we discuss future research directions. METHODS: Using ScienceDirect, ACM Digital Library, and PubMed, 34 research articles were used in this review. A review was conducted using the selected articles with the goal of understanding the current landscape of cyberbullying research. RESULTS: Studies have analyzed correlations between depressive and suicidal ideations in subjects as well as relationships in the social, educational, and financial status of the perpetrators. Studies have explored detection methods for monitoring cyberbullying. Automated detection has yet to become effective and accurate; however, several factors, such as personal background and physical appearance, have been identified to correlate with the likelihood that a person becomes a survivor or perpetrator of web-based cybervictimization. Social support is currently common in recovery efforts but may require diversification for specific applications in web-based incidents. CONCLUSIONS: Relations between social status, age, gender, and behaviors have been discovered that offer new insights into the origins and likeliness of cyberbullying events. Rehabilitation from such events is possible; however, automatic detection is not yet a viable solution for prevention of cyberbullying incidents. Effects such as social exclusion and suicidal ideations are closely tied to incidents of cyberbullying and require further study across various social and demographical populations. New studies should be conducted to explore the experiences of survivors and perpetrators and identify causal links. The breadth of research includes demographics from China, Canada, Taiwan, Iran, the United States, and Namibia. Wider ranges of national populations should be considered in future studies for accurate assessments, given global internet communication technology activity. The studies emphasize the need for formal classification terminology. With formal classification, researchers will have a more definite scope, allowing specific research on a single definable topic rather than on general bullying events and symptoms. Of all the studies, 2 used a longitudinal design for their research methodology. The low number of longitudinal studies leaves gaps between causation and correlation, and further research is required to understand the effects of cyberbullying. Research addressing ongoing victimization is required for the various forms of cyberbullying; social support offers the most effective current standard for prevention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,008 |
| Bibliométrie | 0,014 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».